本文整理汇总了Python中object_detection.meta_architectures.ssd_meta_arch.SSDFeatureExtractor方法的典型用法代码示例。如果您正苦于以下问题：Python ssd_meta_arch.SSDFeatureExtractor方法的具体用法？Python ssd_meta_arch.SSDFeatureExtractor怎么用？Python ssd_meta_arch.SSDFeatureExtractor使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类object_detection.meta_architectures.ssd_meta_arch的用法示例。
在下文中一共展示了ssd_meta_arch.SSDFeatureExtractor方法的7个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: restore_from_classification_checkpoint_fn
# 需要导入模块: from object_detection.meta_architectures import ssd_meta_arch [as 别名]
# 或者: from object_detection.meta_architectures.ssd_meta_arch import SSDFeatureExtractor [as 别名]
def restore_from_classification_checkpoint_fn(self, feature_extractor_scope):
"""Returns a map of variables to load from a foreign checkpoint.
Note that this overrides the default implementation in
ssd_meta_arch.SSDFeatureExtractor which does not work for PNASNet
checkpoints.
Args:
feature_extractor_scope: A scope name for the first stage feature
extractor.
Returns:
A dict mapping variable names (to load from a checkpoint) to variables in
the model graph.
"""
variables_to_restore = {}
for variable in tf.global_variables():
if variable.op.name.startswith(feature_extractor_scope):
var_name = variable.op.name.replace(feature_extractor_scope + '/', '')
var_name += '/ExponentialMovingAverage'
variables_to_restore[var_name] = variable
return variables_to_restore 示例2: restore_from_classification_checkpoint_fn
# 需要导入模块: from object_detection.meta_architectures import ssd_meta_arch [as 别名]
# 或者: from object_detection.meta_architectures.ssd_meta_arch import SSDFeatureExtractor [as 别名]
def restore_from_classification_checkpoint_fn(self, feature_extractor_scope):
"""Returns a map of variables to load from a foreign checkpoint.
Note that this overrides the default implementation in
ssd_meta_arch.SSDFeatureExtractor which does not work for PNASNet
checkpoints.
Args:
feature_extractor_scope: A scope name for the first stage feature
extractor.
Returns:
A dict mapping variable names (to load from a checkpoint) to variables in
the model graph.
"""
variables_to_restore = {}
for variable in variables_helper.get_global_variables_safely():
if variable.op.name.startswith(feature_extractor_scope):
var_name = variable.op.name.replace(feature_extractor_scope + '/', '')
var_name += '/ExponentialMovingAverage'
variables_to_restore[var_name] = variable
return variables_to_restore 开发者ID:ShivangShekhar，项目名称:Live-feed-object-device-identification-using-Tensorflow-and-OpenCV，代码行数:24，代码来源:ssd_pnasnet_feature_extractor.py
示例3: _build_ssd_feature_extractor
# 需要导入模块: from object_detection.meta_architectures import ssd_meta_arch [as 别名]
# 或者: from object_detection.meta_architectures.ssd_meta_arch import SSDFeatureExtractor [as 别名]
def _build_ssd_feature_extractor(feature_extractor_config, is_training,
reuse_weights=None):
"""Builds a ssd_meta_arch.SSDFeatureExtractor based on config.
Args:
feature_extractor_config: A SSDFeatureExtractor proto config from ssd.proto.
is_training: True if this feature extractor is being built for training.
reuse_weights: if the feature extractor should reuse weights.
Returns:
ssd_meta_arch.SSDFeatureExtractor based on config.
Raises:
ValueError: On invalid feature extractor type.
"""
feature_type = feature_extractor_config.type
depth_multiplier = feature_extractor_config.depth_multiplier
min_depth = feature_extractor_config.min_depth
conv_hyperparams = hyperparams_builder.build(
feature_extractor_config.conv_hyperparams, is_training)
if feature_type not in SSD_FEATURE_EXTRACTOR_CLASS_MAP:
raise ValueError('Unknown ssd feature_extractor: {}'.format(feature_type))
feature_extractor_class = SSD_FEATURE_EXTRACTOR_CLASS_MAP[feature_type]
return feature_extractor_class(depth_multiplier, min_depth, conv_hyperparams,
reuse_weights) 示例4: _build_ssd_feature_extractor
# 需要导入模块: from object_detection.meta_architectures import ssd_meta_arch [as 别名]
# 或者: from object_detection.meta_architectures.ssd_meta_arch import SSDFeatureExtractor [as 别名]
def _build_ssd_feature_extractor(feature_extractor_config, is_training,
reuse_weights=None):
"""Builds a ssd_meta_arch.SSDFeatureExtractor based on config.
Args:
feature_extractor_config: A SSDFeatureExtractor proto config from ssd.proto.
is_training: True if this feature extractor is being built for training.
reuse_weights: if the feature extractor should reuse weights.
Returns:
ssd_meta_arch.SSDFeatureExtractor based on config.
Raises:
ValueError: On invalid feature extractor type.
"""
feature_type = feature_extractor_config.type
depth_multiplier = feature_extractor_config.depth_multiplier
min_depth = feature_extractor_config.min_depth
pad_to_multiple = feature_extractor_config.pad_to_multiple
batch_norm_trainable = feature_extractor_config.batch_norm_trainable
use_explicit_padding = feature_extractor_config.use_explicit_padding
use_depthwise = feature_extractor_config.use_depthwise
conv_hyperparams = hyperparams_builder.build(
feature_extractor_config.conv_hyperparams, is_training)
if feature_type not in SSD_FEATURE_EXTRACTOR_CLASS_MAP:
raise ValueError('Unknown ssd feature_extractor: {}'.format(feature_type))
feature_extractor_class = SSD_FEATURE_EXTRACTOR_CLASS_MAP[feature_type]
return feature_extractor_class(is_training, depth_multiplier, min_depth,
pad_to_multiple, conv_hyperparams,
batch_norm_trainable, reuse_weights,
use_explicit_padding, use_depthwise) 示例5: _build_ssd_feature_extractor
# 需要导入模块: from object_detection.meta_architectures import ssd_meta_arch [as 别名]
# 或者: from object_detection.meta_architectures.ssd_meta_arch import SSDFeatureExtractor [as 别名]
def _build_ssd_feature_extractor(feature_extractor_config, is_training,
reuse_weights=None):
"""Builds a ssd_meta_arch.SSDFeatureExtractor based on config.
Args:
feature_extractor_config: A SSDFeatureExtractor proto config from ssd.proto.
is_training: True if this feature extractor is being built for training.
reuse_weights: if the feature extractor should reuse weights.
Returns:
ssd_meta_arch.SSDFeatureExtractor based on config.
Raises:
ValueError: On invalid feature extractor type.
"""
feature_type = feature_extractor_config.type
depth_multiplier = feature_extractor_config.depth_multiplier
min_depth = feature_extractor_config.min_depth
pad_to_multiple = feature_extractor_config.pad_to_multiple
use_explicit_padding = feature_extractor_config.use_explicit_padding
use_depthwise = feature_extractor_config.use_depthwise
conv_hyperparams = hyperparams_builder.build(
feature_extractor_config.conv_hyperparams, is_training)
override_base_feature_extractor_hyperparams = (
feature_extractor_config.override_base_feature_extractor_hyperparams)
if feature_type not in SSD_FEATURE_EXTRACTOR_CLASS_MAP:
raise ValueError('Unknown ssd feature_extractor: {}'.format(feature_type))
feature_extractor_class = SSD_FEATURE_EXTRACTOR_CLASS_MAP[feature_type]
return feature_extractor_class(
is_training, depth_multiplier, min_depth, pad_to_multiple,
conv_hyperparams, reuse_weights, use_explicit_padding, use_depthwise,
override_base_feature_extractor_hyperparams) 示例6: _build_ssd_feature_extractor
# 需要导入模块: from object_detection.meta_architectures import ssd_meta_arch [as 别名]
# 或者: from object_detection.meta_architectures.ssd_meta_arch import SSDFeatureExtractor [as 别名]
def _build_ssd_feature_extractor(feature_extractor_config, is_training,
reuse_weights=None):
"""Builds a ssd_meta_arch.SSDFeatureExtractor based on config.
Args:
feature_extractor_config: A SSDFeatureExtractor proto config from ssd.proto.
is_training: True if this feature extractor is being built for training.
reuse_weights: if the feature extractor should reuse weights.
Returns:
ssd_meta_arch.SSDFeatureExtractor based on config.
Raises:
ValueError: On invalid feature extractor type.
"""
feature_type = feature_extractor_config.type
depth_multiplier = feature_extractor_config.depth_multiplier
min_depth = feature_extractor_config.min_depth
pad_to_multiple = feature_extractor_config.pad_to_multiple
batch_norm_trainable = feature_extractor_config.batch_norm_trainable
conv_hyperparams = hyperparams_builder.build(
feature_extractor_config.conv_hyperparams, is_training)
if feature_type not in SSD_FEATURE_EXTRACTOR_CLASS_MAP:
raise ValueError('Unknown ssd feature_extractor: {}'.format(feature_type))
feature_extractor_class = SSD_FEATURE_EXTRACTOR_CLASS_MAP[feature_type]
return feature_extractor_class(is_training, depth_multiplier, min_depth,
pad_to_multiple, conv_hyperparams,
batch_norm_trainable, reuse_weights) 示例7: _build_ssd_feature_extractor
# 需要导入模块: from object_detection.meta_architectures import ssd_meta_arch [as 别名]
# 或者: from object_detection.meta_architectures.ssd_meta_arch import SSDFeatureExtractor [as 别名]
def _build_ssd_feature_extractor(feature_extractor_config, is_training,
reuse_weights=None):
"""Builds a ssd_meta_arch.SSDFeatureExtractor based on config.
Args:
feature_extractor_config: A SSDFeatureExtractor proto config from ssd.proto.
is_training: True if this feature extractor is being built for training.
reuse_weights: if the feature extractor should reuse weights.
Returns:
ssd_meta_arch.SSDFeatureExtractor based on config.
Raises:
ValueError: On invalid feature extractor type.
"""
feature_type = feature_extractor_config.type
depth_multiplier = feature_extractor_config.depth_multiplier
min_depth = feature_extractor_config.min_depth
pad_to_multiple = feature_extractor_config.pad_to_multiple
use_explicit_padding = feature_extractor_config.use_explicit_padding
use_depthwise = feature_extractor_config.use_depthwise
conv_hyperparams = hyperparams_builder.build(
feature_extractor_config.conv_hyperparams, is_training)
override_base_feature_extractor_hyperparams = (
feature_extractor_config.override_base_feature_extractor_hyperparams)
if feature_type not in SSD_FEATURE_EXTRACTOR_CLASS_MAP:
raise ValueError('Unknown ssd feature_extractor: {}'.format(feature_type))
feature_extractor_class = SSD_FEATURE_EXTRACTOR_CLASS_MAP[feature_type]
kwargs = {
'is_training':
is_training,
'depth_multiplier':
depth_multiplier,
'min_depth':
min_depth,
'pad_to_multiple':
pad_to_multiple,
'conv_hyperparams_fn':
conv_hyperparams,
'reuse_weights':
reuse_weights,
'use_explicit_padding':
use_explicit_padding,
'use_depthwise':
use_depthwise,
'override_base_feature_extractor_hyperparams':
override_base_feature_extractor_hyperparams
}
if feature_extractor_config.HasField('fpn'):
kwargs.update({
'fpn_min_level': feature_extractor_config.fpn.min_level,
'fpn_max_level': feature_extractor_config.fpn.max_level,
})
return feature_extractor_class(**kwargs)